Analysis of maternal polymorphisms in arsenic (+3 oxidation state)-methyltransferase AS3MT and fetal sex in relation to arsenic metabolism and infant birth outcomes: Implications for risk analysis

Arsenic (+3 oxidation state) methyltransferase (AS3MT) is the key enzyme in the metabolism of inorganic arsenic (iAs). Polymorphisms of AS3MT influence adverse health effects in adults, but little is known about their role in iAs metabolism in pregnant women and infants. The relationships between seven single nucleotide polymorphisms (SNPs) in AS3MT and urinary concentrations of iAs and its methylated metabolites were assessed in mother-infant pairs of the Biomarkers of Exposure to ARsenic (BEAR) cohort. Maternal alleles for five of the seven SNPs (rs7085104, rs3740400, rs3740393, rs3740390, and rs1046778) were associated with urinary concentrations of iAs metabolites, and alleles for one SNP (rs3740393) were associated with birth outcomes/measures. These associations were strongly dependent upon the male sex of the fetus but independent of fetal genotype for AS3MT. These data highlight a potential sex-dependence of the relationships among maternal genotype, iAs metabolism and infant health outcomes

As "Ómicas" como ferramenta no estudo da Saúde Ambiental

Deaths caused by environmental pollution are agrowing public health issue. Most of the premature deaths related to pollution are caused by non communicable diseases such as chronic obstructive pulmonary disease, type-2 diabetes, cardiovascular disease and cancer. They are considered complex diseases because of their multicausality and the various mechanisms involved in their emergence and evolution.Knowledge of disease-causing mechanismsis increasing and the identification of disease-associated biomarkers improving thanks to technological progress, in particular that of the technologiesthat are applied to the measurement and interpretation of molecular components—the so-called “Omics” technologies. These technologies have allowed the cellular causes of some complex diseases to be identified: genetic variants of susceptibility or protection to pollutants (Genomics), as well as changes in the DNA (Epigenomics) and their effects on the process of transcription of specific genes for repair, on metabolism or on the non-coding RNA associated with diseases (Transcriptomics). In addition, Proteomics and Metabolomics do not cease to provide information on proteins and metabolites involved in disease processes. Bioinformatics has evolved parallel to the development of omics, which has allowed the results of the measurements of hundreds of molecules to be interpreted and organized into networks that show the relationships among them.Omics are mainly used to develop disease risk models based on population studies, but information on genomes, transcriptomes, epigenomes, microbiomes, proteomes and metabolomesis also used to decipher diseases in order to facilitate prognosis and guide patient treatment, thus contributing to personalized, precision medicine. However, their clinical application is still limited by their cost and their technical, regulatory and ethical implications.Las muertes provocadas por la contaminación ambiental son un problema de salud pública en incremento. La mayoría de las muertes prematuras provocadas por la contaminación son enfermedades no transmisibles, como enfermedad pulmonar obstructiva crónica, diabetes tipo 2, enfermedades cardiovasculares y cáncer. Estas son consideradas enfermedades complejas por su multicausalidad y los diversos mecanismos involucrados en su aparición y evolución. El conocimiento del mecanismo de producción de la enfermedad, y la identificación de biomarcadores asociados a enfermedad está avanzando gracias al avance de la tecnología, y específicamente de la tecnología aplicada a medición e interpretación de componentes moleculares: las tecnologías “ÓMICAS”. Estas han permitido identificar causas celulares de algunas enfermedades complejas: variantes genéticas de susceptibilidad o protección a agentes contaminantes (Genómica), así como cambios sobre el ADN (Epigenómica) y sus efectos en el proceso de transcripción de genes específicos de reparación, metabolismo o bien RNA no codificante asociado a enfermedades (Transcriptómica); además la Proteómica y la Metabolómica aportan constante información sobre las proteínas y metabolitos involucrados en los procesos de enfermedad. Paralelo al desarrollo de las tecnologías ómicas ha evolucionado la bioinformática, que ha permitido la interpretación de los resultados de mediciones de cientos de moléculas para organizarlos en redes que traducen las relaciones entre ellas. Las tecnologías ómicas se aplican principalmente para determinar modelos de riesgo de enfermedad en base a estudios poblacionales, pero también la información del genoma, transcriptoma, el epigenoma, el microbioma, el proteoma y el metaboloma se utilizarán para ayudar a descifrar la enfermedad a fin de facilitar el pronóstico y guiar el tratamiento de pacientes, ayudando a la medicina individualizada y medicina de precisión. Sin embargo, su aplicación clínica está aún limitada por el costo y las implicaciones técnicas, regulatorias y éticas.As mortes causadas pela poluição ambiental sãoum problema de saúde pública crescente. A maioria das mortes prematuras causadas por contaminação sãodoençasnãotransmissíveis, como doença pulmonar obstrutiva crónica, diabetes tipo 2, doenças cardiovasculares e cancro. Estas são consideradas doenças complexas pela sua multicausalidade e pelos vários mecanismos envolvidos no seu aparecimento e evolução. O conhecimento do mecanismo de produção da doença e a identificação de biomarcadores associados à doençaestá a avançar graçasao desenvolvimento da tecnologia e, especificamente, à tecnologia aplicada à medição e interpretação de componentes moleculares: as tecnologias “ÓMICAS”. Estas permitiram identificar as causas celulares de algumasdoenças complexas: variantes genéticas de suscetibilidade ouproteção a agentes contaminantes (Genómica), bem como alterações no DNA (Epigenética) e os seusefeitos no processo de transcrição de genes específicos de reparação, metabolismo ou RNAnão-codificanteassociado a doenças (Transcriptómica);acresce a Proteómica e a Metabolómica que fornecem informação sobre as proteínas e metabólitosenvolvidos nos processos de doença. Paralelamente ao desenvolvimento das novas técnicas biotecnológicas, geralmente denominadas por “Ómicas”, evoluiu a bioinformática, o que permitiu a interpretação dos resultados das análises de centenas de moléculas para organizá-las em redes que traduzem as relações entre elas. As tecnologias “Ómicas” aplicam-se principalmente para determinar modelos de risco de doença com base em estudos populacionais, mas igualmente a informação do genoma, do transcriptoma, do epigenoma, do microbioma, do proteoma e do metaboloma será usada para ajudar a decifrar a doença, a fim de facilitar o prognóstico e orientar o tratamento dos pacientes, auxiliado a medicina individualizada e a medicina de precisão. No entanto, a sua aplicação clínica ainda é limitada pelo custo e implicações técnicas, regulamentares e éticas

Omics as Environmental Health study tools

Deaths caused by environmental pollution are agrowing public health issue. Most of the premature deaths related to pollution are caused by non communicable diseases such as chronic obstructive pulmonary disease, type-2 diabetes, cardiovascular disease and cancer. They are considered complex diseases because of their multicausality and the various mechanisms involved in their emergence and evolution.Knowledge of disease-causing mechanismsis increasing and the identification of disease-associated biomarkers improving thanks to technological progress, in particular that of the technologiesthat are applied to the measurement and interpretation of molecular components—the so-called “Omics” technologies. These technologies have allowed the cellular causes of some complex diseases to be identified: genetic variants of susceptibility or protection to pollutants (Genomics), as well as changes in the DNA (Epigenomics) and their effects on the process of transcription of specific genes for repair, on metabolism or on the non-coding RNA associated with diseases (Transcriptomics). In addition, Proteomics and Metabolomics do not cease to provide information on proteins and metabolites involved in disease processes. Bioinformatics has evolved parallel to the development of omics, which has allowed the results of the measurements of hundreds of molecules to be interpreted and organized into networks that show the relationships among them.Omics are mainly used to develop disease risk models based on population studies, but information on genomes, transcriptomes, epigenomes, microbiomes, proteomes and metabolomesis also used to decipher diseases in order to facilitate prognosis and guide patient treatment, thus contributing to personalized, precision medicine. However, their clinical application is still limited by their cost and their technical, regulatory and ethical implications.</p

Maternal serum concentrations of one-carbon metabolism factors modify the association between biomarkers of arsenic methylation efficiency and birth weight

Background Inorganic arsenic (iAs) is a ubiquitous metalloid and drinking water contaminant. Prenatal exposure is associated with birth outcomes across multiple studies. During metabolism, iAs is sequentially methylated to mono- and di-methylated arsenical species (MMAs and DMAs) to facilitate whole body clearance. Inefficient methylation (e.g., higher urinary % MMAs) is associated with increased risk of certain iAs-associated diseases. One-carbon metabolism factors influence iAs methylation, modifying toxicity in adults, and warrant further study during the prenatal period. The objective of this study was to evaluate folate, vitamin B12, and homocysteine as modifiers of the relationship between biomarkers of iAs methylation efficiency and birth outcomes. Methods Data from the Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort (2011–2012)  with maternal urine and cord serum arsenic biomarkers and maternal serum folate, vitamin B12, and homocysteine concentrations were utilized. One-carbon metabolism factors were dichotomized using clinical cutoffs and median splits. Multivariable linear regression models were fit to evaluate associations between each biomarker and birth outcome overall and within levels of one-carbon metabolism factors. Likelihood ratio tests of full and reduced models were used to test the significance of statistical interactions on the additive scale (α = 0.10). Results Among urinary biomarkers, % U-MMAs was most strongly associated with birth weight (β = − 23.09, 95% CI: − 44.54, − 1.64). Larger, more negative mean differences in birth weight were observed among infants born to women who were B12 deficient (β = − 28.69, 95% CI: − 53.97, − 3.42) or experiencing hyperhomocysteinemia (β = − 63.29, 95% CI: − 154.77, 28.19). Generally, mean differences in birth weight were attenuated among infants born to mothers with higher serum concentrations of folate and vitamin B12 (or lower serum concentrations of homocysteine). Effect modification by vitamin B12 and homocysteine was significant on the additive scale for some associations. Results for gestational age were less compelling, with an approximate one-week mean difference associated with C-tAs (β = 0.87, 95% CI: 0, 1.74), but not meaningful otherwise. Conclusions Tissue distributions of iAs and its metabolites (e.g., % MMAs) may vary according to serum concentrations of folate, vitamin B12 and homocysteine during pregnancy. This represents a potential mechanism through which maternal diet may modify the harms of prenatal exposure to iAs

Neonatal Metabolomic Profiles Related to Prenatal Arsenic Exposure

Prenatal inorganic arsenic (iAs) exposure is associated with health effects evident at birth and later in life. An understanding of the relationship between prenatal iAs exposure and alterations in the neonatal metabolome could reveal critical molecular modifications, potentially underpinning disease etiologies. In this study, nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was used to identify metabolites in neonate cord serum associated with prenatal iAs exposure in participants from the Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort, in GoÌmez Palacio, Mexico. Through multivariable linear regression, ten cord serum metabolites were identified as significantly associated with total urinary iAs and/or iAs metabolites, measured as %iAs, %monomethylated arsenicals (MMAs), and %dimethylated arsenicals (DMAs). A total of 17 metabolites were identified as significantly associated with total iAs and/or iAs metabolites in cord serum. These metabolites are indicative of changes in important biochemical pathways such as vitamin metabolism, the citric acid (TCA) cycle, and amino acid metabolism. These data highlight that maternal biotransformation of iAs and neonatal levels of iAs and its metabolites are associated with differences in neonate cord metabolomic profiles. The results demonstrate the potential utility of metabolites as biomarkers/indicators of in utero environmental exposure

Prenatal Arsenic Exposure and the Epigenome: Identifying Sites of 5-methylcytosine Alterations that Predict Functional Changes in Gene Expression in Newborn Cord Blood and Subsequent Birth Outcomes

Prenatal exposure to inorganic arsenic (iAs) is detrimental to the health of newborns and increases the risk of disease development later in life. Here we examined a subset of newborn cord blood leukocyte samples collected from subjects enrolled in the Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort in Gómez Palacio, Mexico, who were exposed to a range of drinking water arsenic concentrations (0.456–236 µg/l). Changes in iAs-associated DNA 5-methylcytosine methylation were assessed across 424 935 CpG sites representing 18 761 genes and compared with corresponding mRNA expression levels and birth outcomes. In the context of arsenic exposure, a total of 2919 genes were identified with iAs-associated differences in DNA methylation. Site-specific analyses identified DNA methylation changes that were most predictive of gene expression levels where CpG methylation within CpG islands positioned within the first exon, the 5′ untranslated region and 200 bp upstream of the transcription start site yielded the most significant association with gene expression levels. A set of 16 genes was identified with correlated iAs-associated changes in DNA methylation and mRNA expression and all were highly enriched for binding sites of the early growth response (EGR) and CCCTC-binding factor (CTCF) transcription factors. Furthermore, DNA methylation levels of 7 of these genes were associated with differences in birth outcomes including gestational age and head circumference.These data highlight the complex interplay between DNA methylation, functional changes in gene expression and health outcomes and underscore the need for functional analyses coupled to epigenetic assessments

Follow-up study on lead exposure in children living in a smelter community in northern Mexico

<p>Abstract</p> <p>Background</p> <p>To study the changes of children lead exposure in the city of Torreon during the last five years, after environmental and public health interventions, using the timeline of lead in blood concentration as the biomarker of exposure and its relation to lead in soil concentrations.</p> <p>Methods</p> <p>This follow-up study started in 2001 and consisted of 232 children living in nine neighborhoods in Torreon. Children were tested at 0, 6, 12 and 60 months. Lead in blood concentrations, Hemoglobin, Zinc-Protoporphyrin, anthropometric measures and socioeconomic status questionnaire was supplied to the parents.</p> <p>Results</p> <p>Median and range of lead in blood concentrations obtained at 0, 6, 12, 60 months were: 10.12 μg/dl (1.9 - 43.8), 8.75 μg/dl (1.85 - 41.45), 8.4 μg/dl (1.7 - 35.8) and 4.4 μg/dl (1.3 - 30.3), respectively. The decrease of lead in blood levels was significantly related to ages 0, 6, 12 and 60 months of the follow-up study. The timeline of B-Pb was associated with the timeline of lead in soil concentrations.</p> <p>Conclusions</p> <p>B-Pb levels have significantly decreased in the group of children studied. This could be explained by a) environmental interventions by authorities and the smelter companies, b) normal changes in hygienic habits as children age and c) lead redistribution from blood to hard tissues.</p

Follow-up study on lead exposure in children living in a smelter community in northern Mexico

Background: To study the changes of children lead exposure in the city of Torreon during the last five years, after environmental and public health interventions, using the timeline of lead in blood concentration as the biomarker of exposure and its relation to lead in soil concentrations. Methods. This follow-up study started in 2001 and consisted of 232 children living in nine neighborhoods in Torreon. Children were tested at 0, 6, 12 and 60 months. Lead in blood concentrations, Hemoglobin, Zinc-Protoporphyrin, anthropometric measures and socioeconomic status questionnaire was supplied to the parents. Results: Median and range of lead in blood concentrations obtained at 0, 6, 12, 60 months were: 10.12 μg/dl (1.9 - 43.8), 8.75 μg/dl (1.85 - 41.45), 8.4 g/dl (1.7 - 35.8) and 4.4 μg/dl (1.3 - 30.3), respectively. The decrease of lead in blood levels was significantly related to ages 0, 6, 12 and 60 months of the follow-up study. The timeline of B-Pb was associated with the timeline of lead in soil concentrations. Conclusions: B-Pb levels have significantly decreased in the group of children studied. This could be explained by a) environmental interventions by authorities and the smelter companies, b) normal changes in hygienic habits as children age and c) lead redistribution from blood to hard tissues

Advancing Dose–Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic

Dose–response functions used in regulatory risk assessment are based on studies of whole organisms and fail to incorporate genetic and metabolic data. Bayesian belief networks (BBNs) could provide a powerful framework for incorporating such data, but no prior research has examined this possibility. To address this gap, we develop a BBN-based model predicting birthweight at gestational age from arsenic exposure via drinking water and maternal metabolic indicators using a cohort of 200 pregnant women from an arsenic-endemic region of Mexico. We compare BBN predictions to those of prevailing slope-factor and reference-dose approaches. The BBN outperforms prevailing approaches in balancing false-positive and false-negative rates. Whereas the slope-factor approach had 2% sensitivity and 99% specificity and the reference-dose approach had 100% sensitivity and 0% specificity, the BBN’s sensitivity and specificity were 71 and 30%, respectively. BBNs offer a promising opportunity to advance health risk assessment by incorporating modern genetic and metabolic data